Anthropic · job-boards.greenhouse.io · checked today
Data Engineer, GTM Systems
Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
Skills, with evidence
- Data modelling
Experience as a data engineer or analytics engineer building and operating production pipelines and data models
must have - Data quality
Operate what you build as a living system, with observability, data quality, cost, security, and access controls designed in, so problems surface before the field sees them
must have - Python
Strong SQL and Python, and experience with modern transformation and orchestration tooling on a cloud data warehouse
must have - SQL
Strong SQL and Python, and experience with modern transformation and orchestration tooling on a cloud data warehouse
must have - Airflow / orchestration
Experience with modern transformation and orchestration tooling on a cloud data warehouse
must have - Cost & performance
Operate what you build as a living system, with observability, data quality, cost, security, and access controls designed in, so problems surface before the field sees them
- Warehousing
Strong SQL and Python, and experience with modern transformation and orchestration tooling on a cloud data warehouse
Your plan
- The SQL screen: correct, then fast≈ 1 h
Data quality · SQL
- Average order value by countryFoundations
- Line items per orderFoundations
- Revenue by buyer countryFoundations
- Customers who never orderedFoundations
- Hiring funnel by roleFoundations
- Python: the data-wrangling round≈ 2 h
Python
- Clean spreadsheet headers into column namesFoundations
- Events normalization jobFoundations
- List the distinct composite keysFoundations
- Deduplicate rows, first one winsFoundations
- Remove repeats from a sorted list of partitionsFoundations
- Data modelling: the round most people fail≈ 2 h
Data modelling · Warehousing
- Marketplace core entitiesFoundations
- Cinema seat bookingFoundations
- City parking baysFoundations
- Dating app matchesFoundations
- Food delivery ordersFoundations
- Pipeline design: safe to run twice≈ 3 h
Airflow / orchestration
- After the first one finishesFoundations
- Run it for last TuesdayFoundations
- The history nobody keptFoundations
- The spreadsheet is a dependencyFoundations
- Too slow by morningFoundations
- Spark: read the plan Spark actually ran≈ 2 h
Cost & performance
- HAVING vs WHERE: where does a filter after groupBy actually run?Foundations
- partitionBy("country"): what does partition pruning look like in the plan?Foundations
- Predicate pushdown: equality, ranges, and the filter that cannot be pushedFoundations
- Revenue by country: read the DAG behind one groupByFoundations
- Too many small files: where do they cost you, and how does compaction help?Foundations
- Say it out loud≈ 1 h
25 drills · Foundations + Intermediate≈ 10 hours
Readiness
Counted from drills you have completed anywhere on D8LooP.
leaves in 6 dremoved the moment Anthropic closes it